Depressive disorders are the leading cause of disability worldwide, especially in women. These disorders reach a peak incidence between the ages of 18 and 23 and are prone to recurring and chronic course. Prevention is considered key to reducing their burden, but prevention programs are hindered by limited understanding of who is at risk and what processes drive this risk. A number of risk factors have been identified (family history of psychopathology, maladaptive parenting, antecedent symptoms, personality, negative life events, etc.), but many are not modifiable, and mechanisms linking them to depression onset are unclear. Gene expression promises to increase our ability to predict depression and shed light on depression etiology, because transcription is malleable, provides a functional index of salient genetic risk, and also indexes inputs of depressogenic environmental influences. In another sample, we constructed a gene expression profile that consists of 28 genes and is strongly associated with depression. This proposal builds on R01MH093479, a prospective study of 550 girls designed to elucidate dynamic effects of risk factors, stressors, and biological mechanisms on depression within a developmental context. The original study developed a predictive model for depression based on established risk factors. First, we aim to use gene expression profiles derived in prior research to enhance the power of the predictive model. Second, we will leverage the wealth of data on risk trajectories collected on this cohort to investigate the role of gene expression in depression etiology. Moreover, we will reassess gene expression 3 years later to identify intervening factors that shape expression of depression-relevant genes. Third, we will develop a novel gene expression profile in this cohort specifically for prediction of depression onset. To achieve these aims, we propose to follow the cohort to age 23.5, completing 4 annual phenotypic assessments and 2 gene expression assays (age 20 and 23). The proposed study will break new ground by using gene expression to predict depression onset. We will explicate genetic, environmental, and developmental processes that underpin depression risk. This research will suggest hypotheses for how to intervene with various high risk subgroups that can be tested in prevention studies.